{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://github.com/sithu31296/sota-backbones\n",
    "%cd sota-backbones\n",
    "%pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Available Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python list_models.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from models import *\n",
    "\n",
    "model_name = \"VAN\"\n",
    "model = eval(model_name)('S', pretrained=None)\n",
    "image = torch.randn(1, 3, 224, 224)\n",
    "output = model(image)\n",
    "output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a Model with Pretrained Weights\n",
    "\n",
    "Download a model's weights from [Model Zoo](https://github.com/sithu31296/sota-backbones#benchmarks) and place it in a folder.\n",
    "\n",
    "```python\n",
    "model = eval(\"VAN\")('S', pretrained='checkpoints/van_small_811.pth.tar')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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